A new method for estimating the leaf area index (LAI) in cereal crops based on red–green images taken from above the crop canopy is introduced. The proposed method labels pixels into vegetation and soil classes using a combination of greenness and intensity derived from the red and green colour bands. The intensity feature is included to give better separation of the classes, especially under difficult sunny conditions. The performance of the method was investigated in three field experiments during the period 2004–2006. LAI was estimated using gap fraction inversion with an exponential gap fraction model and an ellipsoidal leaf angle distribution. The LAI estimates were compared with measurements taken using a LAI-2000 Plant Canopy Analyzer in terms of their correlation with results from harvested samples scanned with the LI-3100 Area Meter. The results showed that the method was capable of estimating LAI with a precision similar to that of LAI-2000. Correlations with LAI-values estimated by scanning harvested samples showed coefficient of determination (R2) values of 0.68 and 0.81 for the camera method, compared to 0.78 and 0.90 for the LAI-2000. However, an important feature of the proposed method was that it was able to estimate LAI at an early state of growth (LAI < 1) with R2 in the range 0.71–0.79, whereas methods that look upwards through the canopy cannot be applied in this situation. From the perspective of developing an LAI-sensor suitable for operational use, the method is interesting because the colour information used by the method can be acquired by an ordinary inexpensive colour camera, and the sensor does not need to be colour-calibrated. Article Outline 1. Introduction 2. Methods 2.1. Design of a pixel classification method 2.1.1. Distributions of image pixels in (g,L)-space 2.1.2. Projection of (g,L) and probabilistic labelling 2.1.3. Automatic thresholding procedure 2.2. Gap fraction estimation and inversion 2.3. Experiments 2.3.1. Experiment A 2.3.2. Experiment B 2.3.3. Experiment C 3. Results 3.1. Experiment A 3.2. Experiment B 3.3. Experiment C 4. Discussion 5. Conclusions Acknowledgements Appendix. Modelling the correlation between greenness and brightness References Fig. 1. Simulated image of a vegetation canopy (left), with distribution of pixel greenness and brightness (right). View Within Article